Intelligent Interface: Enhancing Lecture Engagement with Didactic Activity Summaries

TL;DR

This paper presents a machine learning-based tool for automating lecture video analysis, providing summaries and feedback to improve teaching engagement and enable comparison of didactic content.

Contribution

It introduces a novel application that automates lecture analysis using ML models, including speech and video analysis, with a flexible architecture for future enhancements.

Findings

Successful implementation of ML models for didactic feature recognition

Interactive summaries and comparison tools for educators

Enhanced transcription quality with speech recognition

Abstract

Recently, multiple applications of machine learning have been introduced. They include various possibilities arising when image analysis methods are applied to, broadly understood, video streams. In this context, a novel tool, developed for academic educators to enhance the teaching process by automating, summarizing, and offering prompt feedback on conducting lectures, has been developed. The implemented prototype utilizes machine learning-based techniques to recognise selected didactic and behavioural teachers' features within lecture video recordings. Specifically, users (teachers) can upload their lecture videos, which are preprocessed and analysed using machine learning models. Next, users can view summaries of recognized didactic features through interactive charts and tables. Additionally, stored ML-based prediction results support comparisons between lectures based on their didactic content. In the developed application text-based models trained on lecture transcriptions, with enhancements to the transcription quality, by adopting an automatic speech recognition solution are applied. Furthermore, the system offers flexibility for (future) integration of new/additional machine-learning models and software modules for image and video analysis.